Skills You Need to Be a Data Scientist in our data-driven world.
We are still at the early stage, when it comes to Data
Science. This means that, it is critical to encourage those who focus on this
nascent emerging technology, which aimed to deal with our data-driven world
with unmatched opportunities.
From a recent article by Thomas Davenport and D.J. Patil in the Harvard Business Review, to Riley Newman (head of data science at
Airbnb) , who has written a
great post on
Airbnb’s data science hiring process on Quora, Connectikpeople.co, soon Retinknow.ga
salutes the determination of Dave , a data scientist at Airbnb and Udacity.com, to demystify as much as possible, a Data
Scientist in our data-driven world.
Below their great work about 8 data science competencies you should develop:
‘’Basic Tools: No matter what type of company you’re interviewing for, you’re likely going to be expected to know how to use the tools of the trade. This means a statistical programming language, like R or Python, and a database querying language like SQL.
Basic Statistics: At least a basic understanding of statistics is vital as a data scientist. An interviewer once told me that many of the people he interviewed couldn’t even provide the correct definition of a p-value. You should be familiar statistical tests, distributions, maximum likelihood estimators, etc. Think back to your basic stats class! This will also be the case for machine learning, but one of the more important aspects of your statistics knowledge will be understanding when different techniques are (or aren’t) a valid approach. Statistics is important at all company types, but especially data-driven companies where the product is not data-focused and product stakeholders will depend on your help to make decisions and design / evaluate experiments.
Machine Learning: If you’re at a large company with huge amounts of data, or working at a company where the product itself is especially data-driven, it may be the case that you’ll want to be familiar with machine learning methods. This can mean things like k-nearest neighbors, random forests, ensemble methods - all of the machine learning buzzwords. It’s true that a lot of these techniques can be implemented using R or Python libraries - because of this, it’s not necessarily a dealbreaker if you’re not the world’s leading expert on how the algorithms work. More important is to understand the broadstrokes and really understand when it is appropriate to use different techniques.
Multivariable Calculus and Linear Algebra: You may in fact be asked to derive some of the machine learning or statistics results you employ elsewhere in your interview. Even if you’re not, your interviewer may ask you some basic multivariable calculus or linear algebra questions, since they form the basis of a lot of these techniques. You may wonder why a data scientist would need to understand this stuff if there are a bunch of out of the box implementations in sklearn or R. The answer is that at a certain point, it can become worth it for a data science team to build out their own implementations in house. Understanding these concepts is most important at companies where the product is defined by the data and small improvements in predictive performance or algorithm optimization can lead to huge wins for the company.
Data Munging: Often times, the data you’re analyzing is going to be messy and difficult to work with. Because of this, it’s really important to know how to deal with imperfections in data. Some examples of data imperfections include missing values, inconsistent string formatting (e.g., ‘New York’ versus ‘new york’ versus ‘ny’), and date formatting (‘2014-01-01’ vs. ‘01/01/2014’, unix time vs. timestamps, etc.). This will be most important at small companies where you’re an early data hire, or data-driven companies where the product is not data-related (particularly because the latter has often grown quickly with not much attention to data cleanliness), but this skill is important for everyone to have.
Data Visualization & Communication: Visualizing and communicating data is incredibly important, especially at young companies who are making data-driven decisions for the first time or companies where data scientists are viewed as people who help others make data-driven decisions. When it comes to communicating, this means describing your findings or the way techniques work to audiences, both technical and non-technical. Visualization wise, it can be immensely helpful to be familiar with data visualization tools like ggplot and d3.js. It is important to not just be familiar with the tools necessary to visualize data, but also the principles behind visually encoding data and communicating information.
Software Engineering: If you’re interviewing at a smaller company and are one of the first data science hires, it can be important to have a strong software engineering background. You’ll be responsible for handling a lot of data logging, and potentially the development of data-driven products.
Thinking Like A Data Scientist: Companies want to see that you’re a (data-driven) problem solver. That is, at some point during your interview process, you’ll probably be asked about some high level problem - for example, about a test the company may want to run or a data-driven product it may want to develop. It’s important to think about what things are important, and what things aren’t. How should you, as the data scientist, interact with the engineers and product managers? What methods should you use? When do approximations make sense?
Data science is still nascent and ill-defined as a field. Getting a job is as much about finding a company whose needs match your skills as it is developing those skills. This writing is based on my own firsthand experiences - I’d love to hear if you’ve had similar (or contrasting) experiences during your own process’’.
Get started with Udacity's Data Analyst Nanodegree.
Below their great work about 8 data science competencies you should develop:
‘’Basic Tools: No matter what type of company you’re interviewing for, you’re likely going to be expected to know how to use the tools of the trade. This means a statistical programming language, like R or Python, and a database querying language like SQL.
Basic Statistics: At least a basic understanding of statistics is vital as a data scientist. An interviewer once told me that many of the people he interviewed couldn’t even provide the correct definition of a p-value. You should be familiar statistical tests, distributions, maximum likelihood estimators, etc. Think back to your basic stats class! This will also be the case for machine learning, but one of the more important aspects of your statistics knowledge will be understanding when different techniques are (or aren’t) a valid approach. Statistics is important at all company types, but especially data-driven companies where the product is not data-focused and product stakeholders will depend on your help to make decisions and design / evaluate experiments.
Machine Learning: If you’re at a large company with huge amounts of data, or working at a company where the product itself is especially data-driven, it may be the case that you’ll want to be familiar with machine learning methods. This can mean things like k-nearest neighbors, random forests, ensemble methods - all of the machine learning buzzwords. It’s true that a lot of these techniques can be implemented using R or Python libraries - because of this, it’s not necessarily a dealbreaker if you’re not the world’s leading expert on how the algorithms work. More important is to understand the broadstrokes and really understand when it is appropriate to use different techniques.
Multivariable Calculus and Linear Algebra: You may in fact be asked to derive some of the machine learning or statistics results you employ elsewhere in your interview. Even if you’re not, your interviewer may ask you some basic multivariable calculus or linear algebra questions, since they form the basis of a lot of these techniques. You may wonder why a data scientist would need to understand this stuff if there are a bunch of out of the box implementations in sklearn or R. The answer is that at a certain point, it can become worth it for a data science team to build out their own implementations in house. Understanding these concepts is most important at companies where the product is defined by the data and small improvements in predictive performance or algorithm optimization can lead to huge wins for the company.
Data Munging: Often times, the data you’re analyzing is going to be messy and difficult to work with. Because of this, it’s really important to know how to deal with imperfections in data. Some examples of data imperfections include missing values, inconsistent string formatting (e.g., ‘New York’ versus ‘new york’ versus ‘ny’), and date formatting (‘2014-01-01’ vs. ‘01/01/2014’, unix time vs. timestamps, etc.). This will be most important at small companies where you’re an early data hire, or data-driven companies where the product is not data-related (particularly because the latter has often grown quickly with not much attention to data cleanliness), but this skill is important for everyone to have.
Data Visualization & Communication: Visualizing and communicating data is incredibly important, especially at young companies who are making data-driven decisions for the first time or companies where data scientists are viewed as people who help others make data-driven decisions. When it comes to communicating, this means describing your findings or the way techniques work to audiences, both technical and non-technical. Visualization wise, it can be immensely helpful to be familiar with data visualization tools like ggplot and d3.js. It is important to not just be familiar with the tools necessary to visualize data, but also the principles behind visually encoding data and communicating information.
Software Engineering: If you’re interviewing at a smaller company and are one of the first data science hires, it can be important to have a strong software engineering background. You’ll be responsible for handling a lot of data logging, and potentially the development of data-driven products.
Thinking Like A Data Scientist: Companies want to see that you’re a (data-driven) problem solver. That is, at some point during your interview process, you’ll probably be asked about some high level problem - for example, about a test the company may want to run or a data-driven product it may want to develop. It’s important to think about what things are important, and what things aren’t. How should you, as the data scientist, interact with the engineers and product managers? What methods should you use? When do approximations make sense?
Data science is still nascent and ill-defined as a field. Getting a job is as much about finding a company whose needs match your skills as it is developing those skills. This writing is based on my own firsthand experiences - I’d love to hear if you’ve had similar (or contrasting) experiences during your own process’’.
Get started with Udacity's Data Analyst Nanodegree.
